首页> 外文期刊>International Journal of Heat and Fluid Flow >Input-output reduced-order modeling of unsteady flow over an airfoil at a high angle of attack based on dynamic mode decomposition with control
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Input-output reduced-order modeling of unsteady flow over an airfoil at a high angle of attack based on dynamic mode decomposition with control

机译:基于动态模式分解的基于动态模式分解,在翼型中对翼型的输入输出减小阶数模型。

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摘要

Due to the damage caused by stall flutter, the investigation and modeling of the flow over a wind turbine airfoil at high angles of attack are essential. Dynamic mode decomposition (DMD) and dynamic mode decomposition with control (DMDc) are used to analyze unsteady flow and identify the intrinsic dynamics. The DMDc algorithm is found to have an identification problem when the spatial dimension of the training data is larger than the number of snapshots. IDMDc, a variant algorithm based on reduced dimension data, is introduced to identify the precise intrinsic dynamics. DMD, DMDc and IDMDc are all used to decompose the data for unsteady flow over the S809 airfoil that are obtained by numerical simulations. The DMD results show that the dominant feature of a static airfoil is the adjacent shedding vortices in the wake. For an oscillating airfoil, the DMDc results may fail to consider the effect of the input and have an identification problem. IDMDc can alleviate this problem. The dominant IDMDc modes show that the intrinsic flow for the oscillating case is similar to the unsteady flow over the static airfoil. Moreover, the input-output model identified by IDMDc can give better predictions for different oscillating cases than the identified DMDc model.
机译:由于失速颤动引起的损坏,高角度的风力涡轮机翼型上的流动的调查和建模是必不可少的。使用控制(DMDC)的动态模式分解(DMD)和动态模式分解用于分析不稳定的流量并识别内部动态。当训练数据的空间维度大于快照的空间维度时,发现DMDC算法具有识别问题。 IDMDC是一种基于减小的尺寸数据的变体算法,以确定精确的内在动态。 DMD,DMDC和IDMDC都用于分解通过数值模拟获得的S809翼型的非定常流的数据。 DMD结果表明,静态翼型的主导特征是在尾动中的相邻脱落涡流。对于振荡翼型,DMDC结果可能无法考虑输入的效果并具有识别问题。 IDMDC可以缓解这个问题。主导IDMDC模式表明,振荡壳体的固有流量类似于静态翼型上的不稳定流。此外,IDMDC标识的输入输出模型可以提供比识别的DMDC模型的不同振荡案例更好的预测。

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